AIMC Topic: Machine Learning

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Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.

Scientific reports
Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant challenges to public health. Among its various complications, diabetic nephropathy (DN) emerges as a critical microvascular complication associated with high mortality rate...

Machine learning approach for differentiating iron deficiency anemia and thalassemia using random forest and gradient boosting algorithms.

Scientific reports
Formulas based on red blood cell indices have been used to differentiate between iron deficiency anemia (IDA) and thalassemia (Thal). However, they exhibit varying efficiencies. In this study, we aimed to develop a tool for discriminating between IDA...

Machine learning for grading prediction and survival analysis in high grade glioma.

Scientific reports
We developed and validated a magnetic resonance imaging (MRI)-based radiomics model for the classification of high-grade glioma (HGG) and determined the optimal machine learning (ML) approach. This retrospective analysis included 184 patients (59 gra...

Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Influenza viruses are major pathogens responsible for acute respiratory infections in humans, which present with symptoms such as fever, cough, sore throat, muscle pain, and fatigue. While molecular diagnostics remain the gold standard, t...

Commonality and individuality based graph learning network for EEG emotion recognition.

Journal of neural engineering
The interplay between individual differences and shared human characteristics significantly impacts electroencephalogram (EEG) emotion recognition models, yet remains underexplored. To address this, we propose a commonality and individuality-based EE...

Electroencephalography estimates brain age in infants with high precision: Leveraging advanced machine learning in healthcare.

NeuroImage
Changes in the pace of neurodevelopment are key indicators of atypical maturation during early life. Unfortunately, reliable prognostic tools rely on assessments of cognitive and behavioral skills that develop towards the second year of life and afte...

Using explainable machine learning to predict the irritation and corrosivity of chemicals on eyes and skin.

Toxicology letters
Contact with specific chemicals often results in corrosive and irritative responses in the eyes and skin, playing a pivotal role in assessing the potential hazards of personal care products, cosmetics, and industrial chemicals to human health. While ...

Distribution characteristics of volatile organic compounds and its multidimensional impact on ozone formation in arid regions based on machine learning algorithms.

Environmental pollution (Barking, Essex : 1987)
Volatile Organic Compounds (VOCs) are key components of atmospheric pollution and play a critical role in ozone (O) formation. Understanding their distribution and pollution sources is essential to grasping the multifaceted impact of VOCs on O produc...

AI-driven approaches for air pollution modelling: A comprehensive systematic review.

Environmental pollution (Barking, Essex : 1987)
In recent years, air quality levels have become a global issue with the rise of harmful pollutants and their effects on climate change. Urban areas are especially affected by air pollution, resulting in a deterioration of the environment and a surge ...

A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS.

Journal of affective disorders
BACKGROUND: Depression associated with Chronic Obstructive Pulmonary Disease (COPD) is a detrimental complication that significantly impairs patients' quality of life. This study aims to develop an online predictive model to estimate the risk of depr...